Test prompt and model changes on real datasets before release. Compare quality, latency, and cost in one place, and catch regressions before they reach users.
Benchmark-backed ROI
On a benchmarked production task, we reached 98% accuracy parity with GPT-5.4 while reducing token costs by 80%.
EvalForge helps startup AI teams identify the lowest-cost model that still clears their quality bar, before a release reaches users.
Manual checks miss edge cases. Regressions slip into production. Costs rise without visibility. And every model change becomes a bet instead of a measured decision.
Manual spot checks
A few examples in a playground do not tell you what will happen in production.
Silent regressions
Without benchmarks and traceable experiments, quality drops are easy to miss.
Blind tradeoffs
Teams often discover cost and latency problems only after a change is already live.
EvalForge gives your team a repeatable process for deciding what is safe to ship.
Compare variants on the same benchmark and visualize accuracy, latency, and cost in one place.
EvalForge is expanding beyond prompts and single-model runs. We’re designing support for evaluating multi-step agent workflows with the same benchmark discipline, observability, and cost visibility.
Think traces, intermediate decisions, tool usage, failure points, and final outcomes measured in one place before agent behavior reaches production.
Trace-level visibility
Inspect each step an agent takes, not just the final output.
Tool-call review
Evaluate how reliably agents use tools and external systems.
Step-by-step scoring
Measure failure points, recovery behavior, and final task success.
Release confidence
Ship agent workflows with the same rigor as prompt changes.
The questions startup AI teams ask before trusting a new evaluation workflow.
Join the private beta and start reducing model spend, catching regressions earlier, and shipping with benchmark-backed confidence.